In our recent work, we have proposed several novel functions for accurately estimating the correlation and multipleinput-multiple-output (MIMO) capacity for combined spatial and true-polarization diversity (TPD) schemes. In this letter, we deepen TPD schemes, demonstrating that, with this technique, it is possible increase the capacity of the MIMO system. Moreover, we demonstrate that different TPD configurations (as a function of different spatial distributions, a different number of elements, and various distances between elements) have different TPD solutions in order to reach the maximum capacity. That is, it is necessary to choose a different TPD configuration to maximize MIMO capacity in each spatial configuration, especially when the area available for the antennas is very small; this is the case for most wireless devices where the antennas are one of the latest design constraints.
Multiple-input-multiple-output (MIMO) systems and reverberation chambers (RCs) have joined roads in recent years. This is due to the recently developed ability of RCs to produce a controllable environment that reduces the cost, time, and effort of evaluating the performance of MIMO handset antennas. This letter explores one of these new capabilities to evaluate systems that use true polarization diversity (TPD). Through a novel post-processing hybrid tool for the transformation of a Rayleigh-fading environment into a Rician one, the performance of TPD systems for different Rician fading is shown. Different arrays will be used for the purposes of comparison. This investigation demonstrates that TPD improves traditional orthogonal polarization also for Rician fading.Index Terms-Channel capacity, diversity gain, multipleinput-multiple-output (MIMO) systems, polarization diversity.
EMG analyses have several applications, such as identifying muscle excitation patterns during rehabilitation or training plans, or controlling EMG‐driven devices. However, experimental measurements can be time consuming or difficult to obtain. This study presents a simple algorithm to predict EMG signals that can be applied in real time during running, given only the instantaneous vector of kinematics. We hypothesize that the factorization of the kinematics of the skeleton together with the EMG data of calibration subjects could be used to predict EMG data of another subject only using the kinematic information. The results showed that EMG signals of lower‐limb muscles can be predicted accurately in less than a second using this method. Correlation coefficients between predicted and experimental EMG signals were higher than 0.7 in 10 out of 11 muscles for most prediction trials and subjects, and their overall median value was higher than 0.8. These values confirm that this method could be used to accurately predict EMG signals in real time when only kinematics are measured.
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